A Hidden Markov Framework to Capture Human–Machine Interaction in Automated Vehicles
A Hidden Markov Framework to Capture Human–Machine Interaction in Automated Vehicles
复制标题
用于捕获自动驾驶汽车中人机交互的隐马尔可夫框架
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
L. Chuang
中科院分区：
文献类型：
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作者：
C. Janssen;L. Boyle;A. Kun;Wendy Ju;L. Chuang
摘要隐马尔可夫模型框架被引入到形式化的信念，人类可能有关于模式，其中一个半自动车辆的操作。以前的研究已经确定了各种“自动化水平”，这有助于澄清车辆自动化能力和预期操作员参与的不同程度。然而，被设计为在一定自动化水平下运行的车辆实际上可以在其指定水平内的不同自动化模式下运行，并且其操作模式也可能随着时间而改变。当用户无法理解在任何给定时间运行的自动化模式时，可能会出现混淆，并且这种混淆的可能性不会在简单识别自动化级别的模型中被捕获。相比之下，隐马尔可夫模型框架提供了一个系统的和正式的规范，由于不正确的用户信念的模式混淆。该框架与车辆自动化领域各种跨学科方法的理论和实践相一致。因此，它有助于自动化系统和未来运输系统的原则设计和评估。
ABSTRACT A Hidden Markov Model framework is introduced to formalize the beliefs that humans may have about the mode in which a semi-automated vehicle is operating. Previous research has identified various “levels of automation,” which serve to clarify the different degrees of a vehicle’s automation capabilities and expected operator involvement. However, a vehicle that is designed to perform at a certain level of automation can actually operate across different modes of automation within its designated level, and its operational mode might also change over time. Confusion can arise when the user fails to understand the mode of automation that is in operation at any given time, and this potential for confusion is not captured in models that simply identify levels of automation. In contrast, the Hidden Markov Model framework provides a systematic and formal specification of mode confusion due to incorrect user beliefs. The framework aligns with theory and practice in various interdisciplinary approaches to the field of vehicle automation. Therefore, it contributes to the principled design and evaluation of automated systems and future transportation systems.